Forecasting PM2. 5 concentration using a single-dense layer BiLSTM method
In recent times, particulate matter (PM2. 5) is one of the most critical air quality contaminants,
and the rise of its concentration will intensify the hazard of cleanrooms. The forecasting of …
and the rise of its concentration will intensify the hazard of cleanrooms. The forecasting of …
Imputation of rainfall data using the sine cosine function fitting neural network
Missing rainfall data have reduced the quality of hydrological data analysis because they
are the essential input for hydrological modeling. Much research has focused on rainfall …
are the essential input for hydrological modeling. Much research has focused on rainfall …
A comparative study of various methods for handling missing data in UNSODA
Y Fu, H Liao, L Lv - Agriculture, 2021 - mdpi.com
UNSODA, a free international soil database, is very popular and has been used in many
fields. However, missing soil property data have limited the utility of this dataset, especially …
fields. However, missing soil property data have limited the utility of this dataset, especially …
[HTML][HTML] Assessing methods for multiple imputation of systematic missing data in marine fisheries time series with a new validation algorithm
IF Benavides, M Santacruz, JP Romero-Leiton… - Aquaculture and …, 2023 - Elsevier
Time series from fisheries often contain multiple missing data. This is a severe limitation that
prevents using the data for research on population dynamics, stock assessment, forecasting …
prevents using the data for research on population dynamics, stock assessment, forecasting …
Anomaly detection of consumption in hotel units: A case study comparing isolation forest and variational autoencoder algorithms
T Mendes, PJS Cardoso, J Monteiro, J Raposo - Applied Sciences, 2022 - mdpi.com
Buildings are responsible for a high percentage of global energy consumption, and thus, the
improvement of their efficiency can positively impact not only the costs to the companies they …
improvement of their efficiency can positively impact not only the costs to the companies they …
[PDF][PDF] Stock price forecast of macro-economic factor using recurrent neural network
The stock market is one of the investment choices that always have traction from time to time.
Aside from being a means of corporate funding, investing in the stock market can benefit …
Aside from being a means of corporate funding, investing in the stock market can benefit …
Comparative assessment of univariate and multivariate imputation models for varying lengths of missing rainfall data in a humid tropical region: a case study of …
N Kannegowda, S Udayar Pillai, CVNK Kommireddi… - Acta Geophysica, 2024 - Springer
Accurate measurement of meteorological parameters is crucial for weather forecasting and
climate change research. However, missing observations in rainfall data can pose a …
climate change research. However, missing observations in rainfall data can pose a …
A hybrid deep learning model based on LSTM for long-term PM2. 5 prediction
Y Chen, M Wu, R Tang, S Chen, S Chen - Proceedings of the 3rd …, 2021 - dl.acm.org
With the acceleration of industrialization and urbanization, problem of air pollution becomes
an urgent problem to be solved. PM2. 5 is a major pollutant in the atmosphere which is …
an urgent problem to be solved. PM2. 5 is a major pollutant in the atmosphere which is …
Visualizing Missing Data: COVID-2019
K Lavanya, G Raja Gopal, M Bhargavi… - Congress on Intelligent …, 2022 - Springer
In this paper, provided data visualization about missing data and the actual data of COVID-
2019 dataset of Andhra Pradesh. The study, in which applied different types of imputation …
2019 dataset of Andhra Pradesh. The study, in which applied different types of imputation …
Imputation of rainfall data using improved neural network algorithm
Missing rainfall data have reduced the quality of hydrological data analysis because they
are the essential input for hydrological modeling. Much research has focused on rainfall …
are the essential input for hydrological modeling. Much research has focused on rainfall …